# OpTaS: An Optimization-based Task Specification Library for Trajectory Optimization and Model Predictive Control

arXiv: https://arxiv.org/abs/2301.13512 · HTML: https://mlsanity.com/paper/2301.13512/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2023.

## Built on by (most cited first, 5 of 5)

- [ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning](https://mlsanity.com/paper/2406.19741/) (2024)
- [Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration](https://mlsanity.com/paper/2308.02459/) (2023)
- [Grasping Trajectory Optimization with Point Clouds](https://mlsanity.com/paper/2403.05466/) (2024)
- [Hydra: Marker-Free RGB-D Hand-Eye Calibration](https://mlsanity.com/paper/2504.20584/) (2025)
- [Preference-Based Long-Horizon Robotic Stacking with Multimodal Large Language Models](https://mlsanity.com/paper/2509.24163/) (2025)

## Abstract (arXiv)

This paper presents OpTaS, a task specification Python library for Trajectory Optimization (TO) and Model Predictive Control (MPC) in robotics. Both TO and MPC are increasingly receiving interest in optimal control and in particular handling dynamic environments. While a flurry of software libraries exists to handle such problems, they either provide interfaces that are limited to a specific problem formulation (e.g. TracIK, CHOMP), or are large and statically specify the problem in configuration files (e.g. EXOTica, eTaSL). OpTaS, on the other hand, allows a user to specify custom nonlinear constrained problem formulations in a single Python script allowing the controller parameters to be modified during execution. The library provides interface to several open source and commercial solvers (e.g. IPOPT, SNOPT, KNITRO, SciPy) to facilitate integration with established workflows in robotics. Further benefits of OpTaS are highlighted through a thorough comparison with common libraries. An additional key advantage of OpTaS is the ability to define optimal control tasks in the joint space, task space, or indeed simultaneously. The code for OpTaS is easily installed via pip, and the source code with examples can be found at https://github.com/cmower/optas.